The Primacy of Experience in Language Processing: Semantic Priming Is Driven Primarily by Experiential Similarity.
The Primacy of Experience in Language Processing: Semantic Priming Is Driven Primarily by Experiential Similarity.
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经验在语言处理中的首要地位：语义启动主要由经验相似性驱动。
DOI：
10.1101/2023.03.21.533703
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Conant,LisaL
中科院分区：
文献类型：
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作者：
Fernandino,Leonardo;Conant,LisaL
语义记忆的组织，包括单词含义的记忆，长期以来一直是认知科学的中心问题。尽管人们普遍认为词义表征必须以非任意的方式与感觉运动和情感体验相联系，但这种关系的性质仍然存在争议。一种重要的观点认为，词义直接根据其经验内容（即感觉运动和情感表征）来表示。这种观点的反对者认为，词义的表示主要反映了分类结构，即它们与自然类别的关系。此外，最近基于单词共现（即分布）信息的语言模型在模拟人类语言行为方面取得了成功，人们提出这种信息可能在词汇概念的表示中发挥重要作用。我们使用专为表征相似性分析 (RSA) 设计的语义启动范式来定量评估这些理论对大量单词的表征相似性模式的解释程度。至关重要的是，我们使用偏相关 RSA 来解释模型预测之间的相互相关性，这使我们能够首次评估每个模型的独特效果。语义启动主要是由启动项和目标项之间的经验相似性驱动的，没有证据表明分布或分类相似性的独立影响。此外，在部分明确的相似性评级之后，只有经验模型才能解释启动中的独特方差。这些结果支持语义表示的经验说明，并表明，尽管在某些语言任务中表现良好，但此处评估的分布式模型并未编码人类语义系统使用的同类信息。
The organization of semantic memory, including memory for word meanings, has long been a central question in cognitive science. Although there is general agreement that word meaning representations must make contact with sensory-motor and affective experiences in a non-arbitrary fashion, the nature of this relationship remains controversial. One prominent view proposes that word meanings are represented directly in terms of their experiential content (i.e., sensory-motor and affective representations). Opponents of this view argue that the representation of word meanings reflects primarily taxonomic structure, that is, their relationships to natural categories. In addition, the recent success of language models based on word co-occurrence (i.e., distributional) information in emulating human linguistic behavior has led to proposals that this kind of information may play an important role in the representation of lexical concepts. We used a semantic priming paradigm designed for representational similarity analysis (RSA) to quantitatively assess how well each of these theories explains the representational similarity pattern for a large set of words. Crucially, we used partial correlation RSA to account for intercorrelations between model predictions, which allowed us to assess, for the first time, the unique effect of each model. Semantic priming was driven primarily by experiential similarity between prime and target, with no evidence of an independent effect of distributional or taxonomic similarity. Furthermore, only the experiential models accounted for unique variance in priming after partialling out explicit similarity ratings. These results support experiential accounts of semantic representation and indicate that, despite their good performance at some linguistic tasks, the distributional models evaluated here do not encode the same kind of information used by the human semantic system.